In [37]:
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))
import warnings

# Ignore all warnings
warnings.simplefilter("ignore")
# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
In [38]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.offline as px1
import plotly.graph_objects as go
px1.init_notebook_mode()
In [39]:
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
import plotly.express as px

Data Summary¶

In [40]:
d1=pd.read_csv(r"D:\Empty\Kaggle\datasets\Billionaires Statistics Dataset.csv")
d1.head(6)
Out[40]:
rank finalWorth category personName age country city source industries countryOfCitizenship ... cpi_change_country gdp_country gross_tertiary_education_enrollment gross_primary_education_enrollment_country life_expectancy_country tax_revenue_country_country total_tax_rate_country population_country latitude_country longitude_country
0 1 211000 Fashion & Retail Bernard Arnault & family 74.0 France Paris LVMH Fashion & Retail France ... 1.1 $2,715,518,274,227 65.6 102.5 82.5 24.2 60.7 67059887.0 46.227638 2.213749
1 2 180000 Automotive Elon Musk 51.0 United States Austin Tesla, SpaceX Automotive United States ... 7.5 $21,427,700,000,000 88.2 101.8 78.5 9.6 36.6 328239523.0 37.090240 -95.712891
2 3 114000 Technology Jeff Bezos 59.0 United States Medina Amazon Technology United States ... 7.5 $21,427,700,000,000 88.2 101.8 78.5 9.6 36.6 328239523.0 37.090240 -95.712891
3 4 107000 Technology Larry Ellison 78.0 United States Lanai Oracle Technology United States ... 7.5 $21,427,700,000,000 88.2 101.8 78.5 9.6 36.6 328239523.0 37.090240 -95.712891
4 5 106000 Finance & Investments Warren Buffett 92.0 United States Omaha Berkshire Hathaway Finance & Investments United States ... 7.5 $21,427,700,000,000 88.2 101.8 78.5 9.6 36.6 328239523.0 37.090240 -95.712891
5 6 104000 Technology Bill Gates 67.0 United States Medina Microsoft Technology United States ... 7.5 $21,427,700,000,000 88.2 101.8 78.5 9.6 36.6 328239523.0 37.090240 -95.712891

6 rows × 35 columns

In [41]:
data=d1.copy()

Data Exploration¶

In [42]:
print(f"The shape of the DataFrame is:{d1.shape}")
print(f"The size of the DataFrame is:{d1.size}")

The shape of the DataFrame is:(2640, 35)
The size of the DataFrame is:92400
In [43]:
d1.columns
Out[43]:
Index(['rank', 'finalWorth', 'category', 'personName', 'age', 'country',
       'city', 'source', 'industries', 'countryOfCitizenship', 'organization',
       'selfMade', 'status', 'gender', 'birthDate', 'lastName', 'firstName',
       'title', 'date', 'state', 'residenceStateRegion', 'birthYear',
       'birthMonth', 'birthDay', 'cpi_country', 'cpi_change_country',
       'gdp_country', 'gross_tertiary_education_enrollment',
       'gross_primary_education_enrollment_country', 'life_expectancy_country',
       'tax_revenue_country_country', 'total_tax_rate_country',
       'population_country', 'latitude_country', 'longitude_country'],
      dtype='object')
  • Numerical-rank,finalworth,age,
  • Categorial-category,country,city,industries,organization,selfmade,status,gender
  • Mixed- personName,source,birthdate
In [44]:
d1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2640 entries, 0 to 2639
Data columns (total 35 columns):
 #   Column                                      Non-Null Count  Dtype  
---  ------                                      --------------  -----  
 0   rank                                        2640 non-null   int64  
 1   finalWorth                                  2640 non-null   int64  
 2   category                                    2640 non-null   object 
 3   personName                                  2640 non-null   object 
 4   age                                         2575 non-null   float64
 5   country                                     2602 non-null   object 
 6   city                                        2568 non-null   object 
 7   source                                      2640 non-null   object 
 8   industries                                  2640 non-null   object 
 9   countryOfCitizenship                        2640 non-null   object 
 10  organization                                325 non-null    object 
 11  selfMade                                    2640 non-null   bool   
 12  status                                      2640 non-null   object 
 13  gender                                      2640 non-null   object 
 14  birthDate                                   2564 non-null   object 
 15  lastName                                    2640 non-null   object 
 16  firstName                                   2637 non-null   object 
 17  title                                       339 non-null    object 
 18  date                                        2640 non-null   object 
 19  state                                       753 non-null    object 
 20  residenceStateRegion                        747 non-null    object 
 21  birthYear                                   2564 non-null   float64
 22  birthMonth                                  2564 non-null   float64
 23  birthDay                                    2564 non-null   float64
 24  cpi_country                                 2456 non-null   float64
 25  cpi_change_country                          2456 non-null   float64
 26  gdp_country                                 2476 non-null   object 
 27  gross_tertiary_education_enrollment         2458 non-null   float64
 28  gross_primary_education_enrollment_country  2459 non-null   float64
 29  life_expectancy_country                     2458 non-null   float64
 30  tax_revenue_country_country                 2457 non-null   float64
 31  total_tax_rate_country                      2458 non-null   float64
 32  population_country                          2476 non-null   float64
 33  latitude_country                            2476 non-null   float64
 34  longitude_country                           2476 non-null   float64
dtypes: bool(1), float64(14), int64(2), object(18)
memory usage: 704.0+ KB
In [45]:
d1.isnull().sum()
Out[45]:
rank                                             0
finalWorth                                       0
category                                         0
personName                                       0
age                                             65
country                                         38
city                                            72
source                                           0
industries                                       0
countryOfCitizenship                             0
organization                                  2315
selfMade                                         0
status                                           0
gender                                           0
birthDate                                       76
lastName                                         0
firstName                                        3
title                                         2301
date                                             0
state                                         1887
residenceStateRegion                          1893
birthYear                                       76
birthMonth                                      76
birthDay                                        76
cpi_country                                    184
cpi_change_country                             184
gdp_country                                    164
gross_tertiary_education_enrollment            182
gross_primary_education_enrollment_country     181
life_expectancy_country                        182
tax_revenue_country_country                    183
total_tax_rate_country                         182
population_country                             164
latitude_country                               164
longitude_country                              164
dtype: int64

Percentage of null values

In [46]:
missing_df=d1.isnull().sum().to_frame().rename(columns={0:"Missing Values"})
missing_df["Percentage of missing values"]=(round((d1.isnull().sum()*100)/(len(d1)),2).astype(str)+"%")
missing_df
Out[46]:
Missing Values Percentage of missing values
rank 0 0.0%
finalWorth 0 0.0%
category 0 0.0%
personName 0 0.0%
age 65 2.46%
country 38 1.44%
city 72 2.73%
source 0 0.0%
industries 0 0.0%
countryOfCitizenship 0 0.0%
organization 2315 87.69%
selfMade 0 0.0%
status 0 0.0%
gender 0 0.0%
birthDate 76 2.88%
lastName 0 0.0%
firstName 3 0.11%
title 2301 87.16%
date 0 0.0%
state 1887 71.48%
residenceStateRegion 1893 71.7%
birthYear 76 2.88%
birthMonth 76 2.88%
birthDay 76 2.88%
cpi_country 184 6.97%
cpi_change_country 184 6.97%
gdp_country 164 6.21%
gross_tertiary_education_enrollment 182 6.89%
gross_primary_education_enrollment_country 181 6.86%
life_expectancy_country 182 6.89%
tax_revenue_country_country 183 6.93%
total_tax_rate_country 182 6.89%
population_country 164 6.21%
latitude_country 164 6.21%
longitude_country 164 6.21%
In [47]:
d1["country"].unique()
d1["country"].nunique()
Out[47]:
78
In [48]:
d1.describe().transpose()
Out[48]:
count mean std min 25% 50% 75% max
rank 2640.0 1.289159e+03 7.396937e+02 1.000000 6.590000e+02 1.312000e+03 1.905000e+03 2.540000e+03
finalWorth 2640.0 4.623788e+03 9.834241e+03 1000.000000 1.500000e+03 2.300000e+03 4.200000e+03 2.110000e+05
age 2575.0 6.514019e+01 1.325810e+01 18.000000 5.600000e+01 6.500000e+01 7.500000e+01 1.010000e+02
birthYear 2564.0 1.957183e+03 1.328252e+01 1921.000000 1.948000e+03 1.957000e+03 1.966000e+03 2.004000e+03
birthMonth 2564.0 5.740250e+00 3.710085e+00 1.000000 2.000000e+00 6.000000e+00 9.000000e+00 1.200000e+01
birthDay 2564.0 1.209984e+01 9.918876e+00 1.000000 1.000000e+00 1.100000e+01 2.100000e+01 3.100000e+01
cpi_country 2456.0 1.277552e+02 2.645295e+01 99.550000 1.172400e+02 1.172400e+02 1.250800e+02 2.885700e+02
cpi_change_country 2456.0 4.364169e+00 3.623763e+00 -1.900000 1.700000e+00 2.900000e+00 7.500000e+00 5.350000e+01
gross_tertiary_education_enrollment 2458.0 6.722567e+01 2.134343e+01 4.000000 5.060000e+01 6.560000e+01 8.820000e+01 1.366000e+02
gross_primary_education_enrollment_country 2459.0 1.028585e+02 4.710977e+00 84.700000 1.002000e+02 1.018000e+02 1.026000e+02 1.421000e+02
life_expectancy_country 2458.0 7.812282e+01 3.730099e+00 54.300000 7.700000e+01 7.850000e+01 8.090000e+01 8.420000e+01
tax_revenue_country_country 2457.0 1.254624e+01 5.368625e+00 0.100000 9.600000e+00 9.600000e+00 1.280000e+01 3.720000e+01
total_tax_rate_country 2458.0 4.396334e+01 1.214530e+01 9.900000 3.660000e+01 4.120000e+01 5.910000e+01 1.063000e+02
population_country 2476.0 5.102053e+08 5.542447e+08 38019.000000 6.683440e+07 3.282395e+08 1.366418e+09 1.397715e+09
latitude_country 2476.0 3.490359e+01 1.700350e+01 -40.900557 3.586166e+01 3.709024e+01 4.046367e+01 6.192411e+01
longitude_country 2476.0 1.258316e+01 8.676299e+01 -106.346771 -9.571289e+01 1.045153e+01 1.041954e+02 1.748860e+02
In [49]:
d1["country"].duplicated().sum()
Out[49]:
2561

Data Accessing¶

  • Numerical-rank,finalworth,age,
  • Categorial-category,country,city,industries,organization,selfmade,status,gender
  • Mixed- personName,source,birthdate

Defining the Problems¶

  1. Wealth Distribution:

    • What is the distribution of wealth among different categories?
    • Are there certain industries that dominate the top ranks in terms of wealth?

  2. Geographical Analysis:

    • Which countries have the highest representation among the wealthiest individuals?
    • Is there a correlation between the country of citizenship and the individual's current residence?

  3. Age and Wealth:

    • How does the age of billionaires correlate with their wealth?
    • Are there notable differences in the wealth distribution among different age groups?

  4. Industry Insights:

    • Which industries have the highest concentration of billionaires?
    • Are there specific industries that consistently appear among the top ranks?

  5. Source of Wealth:

    • What are the primary sources of wealth for the billionaires in the dataset?
    • Is there a relationship between the source of wealth and the total worth of an individual?

  6. Country-specific Analysis:

    • Can we identify any economic indicators (e.g., GDP, tax rates) that correlate with the number or wealth of billionaires in a country?
    • How does life expectancy in a country correlate with the wealth of its billionaires?

  7. Population and Wealth:

    • Is there a correlation between the population of a country and the number of billionaires it has?
    • How does the wealth of individuals compare across countries with different population sizes?

  8. Spatial Analysis:

    • Can we visualize the geographical distribution of billionaires on a world map?
    • Are there clusters or patterns in the distribution of billionaires based on latitude and longitude?

  9. Gender Representation:

    • What is the gender distribution among the billionaires in the dataset?
    • Are there notable differences in wealth between male and female billionaires?

  10. Educational Insights:

    • Is there a correlation between the level of education (e.g., tertiary education enrollment) in a country and the wealth of its billionaires?
In [50]:
d1.columns
Out[50]:
Index(['rank', 'finalWorth', 'category', 'personName', 'age', 'country',
       'city', 'source', 'industries', 'countryOfCitizenship', 'organization',
       'selfMade', 'status', 'gender', 'birthDate', 'lastName', 'firstName',
       'title', 'date', 'state', 'residenceStateRegion', 'birthYear',
       'birthMonth', 'birthDay', 'cpi_country', 'cpi_change_country',
       'gdp_country', 'gross_tertiary_education_enrollment',
       'gross_primary_education_enrollment_country', 'life_expectancy_country',
       'tax_revenue_country_country', 'total_tax_rate_country',
       'population_country', 'latitude_country', 'longitude_country'],
      dtype='object')
  1. Wealth Distribution:
    • What is the distribution of wealth among different categories?
    • Are there certain industries that dominate the top ranks in terms of wealth?

Conclusion:¶

Top Industries Ranking in Wealth¶

  1. Automotive.
  2. Technology
  3. Telecom
  4. Logistics
  5. Metals and Mining

Insights:¶

  • These industries dominate the top ranks in wealth, emphasizing their economic significance and success.
  • The financial achievements within each sector contribute to the overall diversity of wealth distribution.
In [51]:
sorted_df=d1.sort_values(by='finalWorth', ascending=False)
sns.barplot(data=sorted_df,x="finalWorth",y="category",palette='viridis',errorbar=None)
plt.title("Wealth distribution based on industries or categories")
Out[51]:
Text(0.5, 1.0, 'Wealth distribution based on industries or categories')
No description has been provided for this image

2. Industry Insights:¶

  • Which industries have the highest concentration of billionaires?
  • Which industries have the highest representation among the individuals in the dataset?

Conclusion:¶

  • Food and beverages,Finance and investments, manufacturing, technology, fashion and retail,energy, healthcare have the highest concentration of billionaires.
  • Finance and investments, manufacturing, technology, fashion and retail are the sectors which contains most male billionaires.
  • Whereas the female billionaires are mainly seen in fashion and retail, food and beverages, and manufacturing industries.
  • Based on the analysis, it can be concluded that the majority of billionaires fall within the age bracket of 50 to 70.
In [52]:
sns.displot(data=d1,x="industries",hue="gender",palette="viridis",element="step")
plt.xticks(rotation="vertical")
plt.title("Industries with highest representation")
Out[52]:
Text(0.5, 1.0, 'Industries with highest representation')
No description has been provided for this image
In [53]:
d1["age"].fillna(d1["age"].mode()[0],inplace=True)
In [54]:
data["age"].fillna(data["age"].mean(),inplace=True)
In [55]:
px.histogram(d1,x="age")
In [56]:
print("The skewness of age column is:",data["age"].skew())
sns.displot(data=data,x="age",kind="hist")

The skewness of age column is: -0.07903682421854166
Out[56]:
<seaborn.axisgrid.FacetGrid at 0x102e50eb150>
No description has been provided for this image

3. Geographical Analysis:¶

  • Which countries have the highest representation among the wealthiest individuals?
  • Is there a correlation between the country of citizenship and the individual's current residence?

Conclusion:¶

  • The top five countries with most billionaires are UNited States, China, India, Germany, and United Kingdom.
  • The high Cramér's V value suggests that there is a substantial relationship between the country of residence and the country of citizenship in the dataset.
In [57]:
d1["country"].value_counts().head(5).to_frame()
Out[57]:
count
country
United States 754
China 523
India 157
Germany 102
United Kingdom 82
In [58]:
d1["country"].value_counts()
Out[58]:
country
United States           754
China                   523
India                   157
Germany                 102
United Kingdom           82
                       ... 
Portugal                  1
Georgia                   1
Eswatini (Swaziland)      1
Uzbekistan                1
Armenia                   1
Name: count, Length: 78, dtype: int64
In [59]:
d1["countryOfCitizenship"].value_counts()
Out[59]:
countryOfCitizenship
United States           735
China                   491
India                   169
Germany                 126
Russia                  104
                       ... 
Belize                    1
Eswatini (Swaziland)      1
Venezuela                 1
Algeria                   1
Panama                    1
Name: count, Length: 77, dtype: int64
In [60]:
from scipy.stats import chi2_contingency

contingency_table = pd.crosstab(d1["country"], d1["countryOfCitizenship"])
chi2, p, _, _ = chi2_contingency(contingency_table)
print(f"Chi-square value: {chi2}, p-value: {p}")

Chi-square value: 132750.7197374479, p-value: 0.0
In [61]:
def cramers_v(confusion_matrix):
    chi2 = chi2_contingency(confusion_matrix)[0]
    n = confusion_matrix.sum().sum()
    phi2 = chi2 / n
    r, k = confusion_matrix.shape
    phi2corr = max(0, phi2 - ((k-1)*(r-1))/(n-1))
    rcorr = r - ((r-1)**2)/(n-1)
    kcorr = k - ((k-1)**2)/(n-1)
    return np.sqrt(phi2corr / min((kcorr-1), (rcorr-1)))

# Calculate Cramér's V
association_strength = cramers_v(contingency_table)
print(f"Cramér's V: {association_strength}")

Cramér's V: 0.8185133547927517

4.Age and Wealth:¶

  • How does the age of billionaires correlate with their wealth?
  • Are there notable differences in the wealth distribution among different age groups?

Conclusion:¶

  • A correlation coefficient of 0.069 is close to zero, suggesting a weak positive correlation. This means that as one variable (age) increases, the other variable (final worth) tends to increase slightly, but the relationship is not strong.
  • Yes, there is a notable difference in the wealth distribution i.e., the people of age less than 50 have net worth less when compared to that of people of age greater than 50.
In [62]:
d1.age.fillna(d1.age.mode()[0],inplace=True)
d1.age.isnull().sum()
Out[62]:
0
In [63]:
from scipy.stats import pearsonr
corr, _ = pearsonr(d1.age,d1.finalWorth)
print('Pearsons correlation: %.3f' % corr)

Pearsons correlation: 0.069
In [64]:
px.bar(d1,x="age",y="finalWorth")

5. Source of Wealth:¶

  • What are the primary sources of wealth for the billionaires in the dataset?
  • Is there a relationship between the source of wealth and the total worth of an individual?

Conclusion¶

In [65]:
d1.source.nunique()
Out[65]:
906
In [66]:
d1.source.value_counts()
Out[66]:
source
Real estate                     151
Investments                      92
Diversified                      91
Pharmaceuticals                  85
Software                         63
                               ... 
Chemical industry                 1
Readymade garments                1
Stock brokerage                   1
Nutrition, wellness products      1
Tyre manufacturing machinery      1
Name: count, Length: 906, dtype: int64
In [70]:
px.histogram(d1,x="source")
In [ ]:
from scipy.stats import kurtosis,skew
kur=kurtosis(d1["age"])
kur
Out[ ]:
-0.1387691447141699
In [ ]:
skew(d1["age"])
Out[ ]:
-0.051088117525369715
In [ ]:
d1.age.mean()
Out[ ]:
65.01363636363637
In [ ]:
sns.displot(data=d1,x="age",kind="kde")
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x102e04759d0>
No description has been provided for this image
In [ ]:
kurtosis(d1["finalWorth"])
Out[ ]:
144.7967937167552
In [ ]:
skew(d1["finalWorth"])
Out[ ]:
10.006677577919163
In [ ]:
sns.displot(data=d1,x="finalWorth",kind="kde")
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x102e079e610>
No description has been provided for this image
In [ ]:
!jupyter nbconvert --to html  billionaires-data-visualization.ipynb

[NbConvertApp] Converting notebook billionaires-data-visualization.ipynb to html
C:\Users\Naveen\AppData\Local\Programs\Python\Python311\Lib\site-packages\nbformat\__init__.py:93: MissingIDFieldWarning: Code cell is missing an id field, this will become a hard error in future nbformat versions. You may want to use `normalize()` on your notebooks before validations (available since nbformat 5.1.4). Previous versions of nbformat are fixing this issue transparently, and will stop doing so in the future.
  validate(nb)
C:\Users\Naveen\AppData\Local\Programs\Python\Python311\share\jupyter\nbconvert\templates\base\display_priority.j2:32: UserWarning: Your element with mimetype(s) dict_keys(['application/vnd.plotly.v1+json']) is not able to be represented.
  {%- elif type == 'text/vnd.mermaid' -%}
[NbConvertApp] WARNING | Alternative text is missing on 5 image(s).
[NbConvertApp] Writing 4163755 bytes to billionaires-data-visualization.html
In [ ]: